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Neural Networks Wrapper for TensorFlow

Project Description
# nn-wtf - Neural Networks Wrapper for TensorFlow

nn-wtf aims at providing a convenience wrapper to Google's
[TensorFlow]( machine learning library.
Its focus is on making neural networks easy to set up, train and use.

The library is in pre-alpha right now and does not do anything seriously useful

## Installation

nn-wtf runs under Python3.4 and above.

### Dependencies

You need to install TensorFlow manually. The installation process depends on
your system. Install the version of TensorFlow built for Python 3.4.

for details.

Example installation for Linux without GPU support:
$ pip install --upgrade

### NN-WTF itself
$ pip install nn_wtf

## Documentation

Sorry the documentation is absolutely minimal at this point. More useful
documentation will be ready by the time this package reaches alpha status.

### List of useful classes and methods

* `NeuralNetworkGraph`: Base class for defining and training neural networks
* `__init__(self, input_size, layer_sizes, output_size, learning_rate)`
* `set_session(self, session=None)`
* `train(self, data_sets, max_steps, precision, steps_between_checks, run_as_check, batch_size)`
* `get_predictor().predict(input_data)`
* `MNISTGraph`: Subclass of NeuralNetworkGraph suitable for working on MNIST data
* `NeuralNetworkOptimizer`: Optimize geometry of a neural network for fast training
* `__init__( self, tested_network, input_size, output_size, training_precision,
layer_sizes, learning_rate, verbose, batch_size)`
* `brute_force_optimal_network_geometry(self, data_sets, max_steps)`

### Usage example

If you want to try it on MNIST data, try this sample program:

from nn_wtf.mnist_data_sets import MNISTDataSets
from nn_wtf.mnist_graph import MNISTGraph

import tensorflow as tf

data_sets = MNISTDataSets('.')
graph = MNISTGraph(
learning_rate=0.1, layer_sizes=(64, 64, 16), train_dir='.'
graph.train(data_sets, max_steps=5000, precision=0.95)

image_data = MNISTDataSets.read_one_image_from_url(
prediction = graph.get_predictor().predict(image_data)
assert prediction == 7

>From there on, you are on your own for now. More functionality and documentation
to come.

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